Application of machine learning to agricultural soil data

Agriculture is a major sector in the Indian economy. One key advantage of classification and prediction of soil parameters is to save time of specialized technicians developing expensive chemical analysis. In this context, this PhD thesis has been developed in three stages: 1. Classification for soil data: we used chemical soil measurements to classify many relevant soil parameters: village-wise fertility indices; soil pH and type; soil nutrients, in order to recommend suitable amounts of fertilizers; and preferable crop. 2. Regression for generic data: we developed an experimental comparison of many regressors to a large collection of generic datasets selected from the University of California at Irving (UCI) machine learning repository. 3. Regression for soil data: We applied the regressors used in stage 2 to the soil datasets, developing a direct prediction of their numeric values. The accuracy of the prediction was evaluated for the ten soil problems, as an alternative to the prediction of the quantified values (classification) developed in stage 1

Systematic approach to Nayanabhighata w.s.r. to ocular trauma

It is said that if vision is lost the whole world becomes blind for that person. Hence for every individual protecting his sense of vision is very important for his existence. Acharya Sushruta has contributed more with regards to Nayanabhighata. Many Acharyas explains the treatment methodology which is useful for Nayanabhighta since Vedic periods as we have reference of replacement of injured eye with artificial eye in Rigveda. For treating the Nayanabhighata, many Kriyakalpas are used extensively which are mentioned in Shalakyatantra which are basic treatment modalities. Ocular trauma is a term given to an eye injury that occurs because of direct blow to the eye. The magnitude varies from a normal black eye to sport injury to a serious case of blood collection between the cornea and iris. The classification,site of injuries of ocular trauma are discussed in this

SYNTHESIS AND BIOLOGICAL EVALUATION OF NOVEL THIAZOLE-PYRAZOLE INTEGRATED CHALCONES AS ANTIOXIDANT AND ANTI-INFLAMMATORY AGENTS

Objective: The objective of the present study was to synthesize the thiazole-pyrazole integrated chalcones and their in vitro antioxidant and anti- inflammatory evaluation. Methods: The designed hybrid thiazole-pyrazole integrated chalcones (3a-j) were synthesized by Claisen–Schmidt reaction of substituted 1-(4-methyl-2-phenylthiazol-5-yl) ethanone and substituted pyrazole aldehyde in the presence of 10% NaOH in ethanol solvent under reflux condition. The chemical structures of synthesized compounds were confirmed by IR, 1H nuclear magnetic resonance (NMR), 13C NMR, and high- resolution mass spectra. Results: All the title compounds were screened for their in vitro antioxidant and anti-inflammatory activity. The screening data indicated that tested compounds showed potent antioxidant activity with moderate anti-inflammatory potential. Conclusion: Antioxidant screening data reveal that most of the synthesized compounds possess excellent 1,1-diphenyl-2-picrylhydrazyl and NO radical scavenging activity. Most of the compounds found to possess marked anti-inflammatory potential by effectively inhibiting the heat-induced albumin denaturation

A Study of the Effect of Volume Fraction on Stress Transfer within a Unidirectional Fiber Reinforced Composite Having a Broken Fiber

Stress concentration due to flaws in any material are very dangerous. Its understanding is thus very important before practical application of the material. As Fiber Reinforced Composite (FRC) is gaining wide range of application it is necessary to analyze it for stress concentration as one of the parameter. Literature survey reveals that the failure of FRC due to breakage of fiber is a cumulative process. If a fiber breaks stress concentration develops near the failure which leads to failure of other fibers in its vicinity. This process continues until the whole FRC gets failed. This phenomenon is quantified by a parameter called Stress Transfer Coefficient (STC). To analyze FRC generally it is practiced to analyze Representative Volume Element (RVE) which is a representation of the complete FRC. Another important aspect which is considered while analyzing FRC is the distribution of the fiber. Ideally the distribution of fibers should be uniform but no manufacturing process guarantees uniform distribution. Thus while analyzing FRC it is a good practice to generate RVE with randomly distributed fibers. In this paper an attempt is made to attain the relationship between volume fraction and STC. FRC with unidirectional fiber orientation is considered. RVEs with different volume fraction are analyzed. A new method is implemented to generate RVE with randomly distributed fibers. RVEs are so generated that it contains a broken fiber at its geometrical center and other fibers surrounding it. The RVE is loaded along the fiber direction

Selective promoting activity of phorbol myristate acetate in experimental skin carcinogenesis.

Experiments were undertaken to study the effect of promotion treatment on epidermal tumour induction pattern in precancerous mouse skin. Swiss albino mice were given a single s.c. injection of 0-5 mg 20-methylcholanthrene in the right scapular region. Six weeks later, 1-83 nmol of phorbol myristate acetate (PMA) was applied biweekly on the reactive skin. Histopathology of the induced tumours showed early appearance of squamous cell carcinomas and rhabdomyosarcomas. Fibrosarcoma, the most common tumour type induced on MCA injection alone, was absent. Trichoepithelioma, a benign tumour, was induced in PMA-treated mice. This gives new evidence of the selective action of PMA, enhancing the induction of epithelial and muscle tumours, with concurrent inhibition of fibroblastic tumours

Effect of Volume Fraction and Fiber Distribution on Stress Transfer in a Stochastic Framework of Continuous Fiber Composite: A Micromechanical Study

In fiber Reinforced Composites (FRC) fiber breakage is a common phenomenon resulting in stress concentration. This high stress gets transfer in the vicinity of the breakage which is quantified by Stress Transfer Coefficient (STC). In this paper, an attempt is made to check the effect of fiber volume fraction and the distribution of the fibers on STC and ineffective length. The fiber volume fraction is changed considering three cases: 1) by changing the number of fibers, 2) by changing the dimension of the Represntative Volume Element (RVE) and 3) by changing the fiber radius. Cases with change in dimension of RVE and change in fiber radius, periodic and semi-random arrangents of fibers are considered. From the analysis of 200 RVE's for each volume fraction in random and semi-random arrangements, it is observed that the distribution of STC does not follow any standard distribution, even if the fiber arrangement follows the normal distribution. The fiber cross-sectional dimension plays a critical role in regaining the broken fiber strength. The periodic arrangement of fibers can be said to be beneficial over the random arrangement considering the stress transfer from the broken fiber

Machine learning for brain stroke: a review

Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically. A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019. Support Vector Machine (SVM) is obtained as optimal models in 10 studies for stroke problems. Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Similarly, CT images are a frequently used dataset in stroke. Finally SVM and Random Forests are efficient techniques used under each category. The present study showcases the contribution of various ML approaches applied to brain stroke.info:eu-repo/semantics/publishedVersio